2024
Integration of a continuously varying image-space PSF for a dual-panel ultra-high TOF-PET scanner
Chemli Y, Marin T, Orehar M, Dolenec R, Normandin M, Gascón D, Gola A, Grogg K, Pavón G, Razdevsek G, Pestotnik R, Fakhri G. Integration of a continuously varying image-space PSF for a dual-panel ultra-high TOF-PET scanner. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10656225.Peer-Reviewed Original ResearchGaussian mixture modelGaussian process regressionPoint spread functionAccurate image reconstructionMaximum likelihood estimation maximizationShift-variant convolutionsImage reconstructionMixture modelProcess regressionEstimation maximizationTime-of-flight (TOFPanel architectureSpread functionArchitectureParameter interpolationHigh resolution time-of-flight (TOFTOF-PET scannerBrain phantomFitting processPositron emission tomography scannerSimulated point sourcesConvolutionAlgorithmEffective diagnosisSize benefitsMultiscale homogenized constrained mixture model of the bio-chemo-mechanics of soft tissue growth and remodeling
Paukner D, Humphrey J, Cyron C. Multiscale homogenized constrained mixture model of the bio-chemo-mechanics of soft tissue growth and remodeling. Biomechanics And Modeling In Mechanobiology 2024, 23: 2115-2136. PMID: 39419845, PMCID: PMC11554721, DOI: 10.1007/s10237-024-01884-w.Peer-Reviewed Original ResearchConstrained mixture modelsNonlinear continuum mechanicsSoft biological tissuesChemo-mechanical interactionsSolid mechanicsChemo-mechanical couplingContinuum mechanicsOrdinary differential equationsSignal processingBiological tissuesMixture modelDifferential equationsEquationsSimulate many casesTissue growthOrgan-scaleComputational analysis of heart valve growth and remodeling after the Ross procedure
Middendorp E, Braeu F, Baaijens F, Humphrey J, Cyron C, Loerakker S. Computational analysis of heart valve growth and remodeling after the Ross procedure. Biomechanics And Modeling In Mechanobiology 2024, 23: 1889-1907. PMID: 39269523, PMCID: PMC11554944, DOI: 10.1007/s10237-024-01874-y.Peer-Reviewed Original ResearchRoss procedureBlood pressure controlRoot dilatationHomogeneous mixture modelPatient's own pulmonary valveMechanical homeostasisPressure controlAortic heart valvesPublished clinical studiesConstrained mixture modelsHemodynamic environmentPulmonary autograftPulmonary valveLeaflet elongationPressure conditionsClinical studiesG&RHemodynamic loadTissue compositionValve growthTissue depositionMixture modelDilatationHeart valvesAutograft
2022
Measurement errors in Gaussian mixture models using high-dimensional air pollution constituents data
Zhou X, Xu L, Fang J, Spiegelman D. Measurement errors in Gaussian mixture models using high-dimensional air pollution constituents data. ISEE Conference Abstracts 2022, 2022 DOI: 10.1289/isee.2022.o-sy-074.Peer-Reviewed Original Research
2017
Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection
Marczyk M. Mixture Modeling of 2-D Gel Electrophoresis Spots Enhances the Performance of Spot Detection. IEEE Transactions On NanoBioscience 2017, 16: 91-99. PMID: 28278480, DOI: 10.1109/tnb.2017.2676725.Peer-Reviewed Original Research
2016
Improved Detection of 2D Gel Electrophoresis Spots by Using Gaussian Mixture Model
Marczyk M. Improved Detection of 2D Gel Electrophoresis Spots by Using Gaussian Mixture Model. Lecture Notes In Computer Science 2016, 9683: 284-294. DOI: 10.1007/978-3-319-38782-6_24.Peer-Reviewed Original ResearchParallel computing capabilitiesSpot detectionGaussian mixture modelGel imagesComputing capabilitiesAutomatic methodEfficient implementationComputational timeBest overall performanceMixture modelAbstract2D gel electrophoresisOverall performanceImagesTrue positionDetectionAlgorithmDatasetSoftwareImplementationCapabilityMethodThousandsAccurate estimatesA Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography
Zhu W, Ouyang J, Rakvongthai Y, Guehl N, Wooten D, Fakhri G, Normandin M, Fan Y. A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography. Medical Physics 2016, 43: 1222-1234. PMID: 26936707, PMCID: PMC5025019, DOI: 10.1118/1.4941010.Peer-Reviewed Original ResearchConceptsPositron emission tomographySpatial mixture modelNearby voxelsMixture modelEmission tomographyDynamic positron emission tomographyK-means methodKinetic modelKinetic parametric imagesOne-compartment kinetic modelNovel algorithmTemporal informationClassification purposesMeasurement of local perfusionLocal perfusionTime activity curvesNormal ROIsTemporal modelBayesian algorithmCardiac studiesMarkov chain Monte CarloParameter estimationNoise regionSimulation experimentsSimulated data sets
2015
Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry
Polanski A, Marczyk M, Pietrowska M, Widlak P, Polanska J. Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry. PLOS ONE 2015, 10: e0134256. PMID: 26230717, PMCID: PMC4521892, DOI: 10.1371/journal.pone.0134256.Peer-Reviewed Original ResearchConceptsGaussian mixture modelingMixture modelGaussian mixture modelPeak detectionProteomic mass spectraFeature extractionPartitioning algorithmEfficient algorithmSignal compressionSoftware packageAlgorithmMain ideaMixture modelingModeling approachModelingDatasetApplicationsDetectionPreliminary resultsDetection efficiencyProteomic datasetsMixture modeling approachModelDifferent typesHigh resolutionSex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes
Crawford FW, Weiss RE, Suchard MA. Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes. The Annals Of Applied Statistics 2015, 9: 572-596. PMID: 26500711, PMCID: PMC4617556, DOI: 10.1214/15-aoas809.Peer-Reviewed Original ResearchLongitudinal count dataImportant statistical problemSelf-reported countsBayesian mixture modelBirth-death processStatistical problemsBayesian hierarchical modelTrue distributionCount dataMixture modelInferential tasksHierarchical modelHeaping processParametersMultiplesModelInferenceEstimationDistributionGridProblemError
2013
Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data
Trentini F, Ji Y, Iwamoto T, Qi Y, Pusztai L, Müller P. Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data. PLOS ONE 2013, 8: e68071. PMID: 23874497, PMCID: PMC3709899, DOI: 10.1371/journal.pone.0068071.Peer-Reviewed Original ResearchDifferential expression analysis for paired RNA-seq data
Chung LM, Ferguson JP, Zheng W, Qian F, Bruno V, Montgomery RR, Zhao H. Differential expression analysis for paired RNA-seq data. BMC Bioinformatics 2013, 14: 110. PMID: 23530607, PMCID: PMC3663822, DOI: 10.1186/1471-2105-14-110.Peer-Reviewed Original Research
2011
Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains
Izu A, Cohen T, Mitnick C, Murray M, De Gruttola V. Bayesian methods for fitting mixture models that characterize branching tree processes: An application to development of resistant TB strains. Statistics In Medicine 2011, 30: 2708-2720. PMID: 21717491, PMCID: PMC3219798, DOI: 10.1002/sim.4287.Peer-Reviewed Original ResearchConceptsCharacterization of uncertaintyBayesian approachBayesian methodsBranching tree modelStatistical methodsMixture modelBranching treeNatural wayPrior informationDrug resistance-conferring mutationsSuch cross-sectional dataDrug-resistant TBResistant TB strainsCombination of antibioticsDrug resistance mutationsMeasurement errorResistance-conferring mutationsTB strainsSingle patientTreatment policyPatientsMultiple drugsDiagnostic specimensCross-sectional dataGenetic mutations
2010
Automated High-Dimensional Flow Cytometric Data Analysis
Pyne S, Hu X, Wang K, Rossin E, Lin T, Maier L, Baecher-Allan C, McLachlan G, Tamayo P, Hafler D, De Jager P, Mesirov J. Automated High-Dimensional Flow Cytometric Data Analysis. Lecture Notes In Computer Science 2010, 6044: 577-577. DOI: 10.1007/978-3-642-12683-3_41.Peer-Reviewed Original Research
2005
The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration
Nadler B, Coifman R. The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration. Journal Of Chemometrics 2005, 19: 107-118. DOI: 10.1002/cem.915.Peer-Reviewed Original ResearchClassical least squaresMultivariate regression algorithmExact mathematical analysisNet analyte signal vectorLeast squaresRegression algorithmSquared errorAdditional error termPartial least squaresFinite trainingMathematical analysisAsymptotic errorDimensional reductionLinear mixture modelMean squared errorInput dataError termMixture modelSignal vectorOverall prediction errorTheoretical justificationP dimensionN samplesLarge calibrationPrediction errorPartial least squares, Beer's law and the net analyte signal: statistical modeling and analysis
Nadler B, Coifman R. Partial least squares, Beer's law and the net analyte signal: statistical modeling and analysis. Journal Of Chemometrics 2005, 19: 45-54. DOI: 10.1002/cem.906.Peer-Reviewed Original ResearchRegression vectorFinite training setNet analyte signal vectorInput-output samplesJoint probability distributionPartial least squaresCommon regression algorithmsNoise-free caseNoise-free samplesRandom variablesRandom realizationsLinear multivariate modelProbability distributionAsymptotic optimalityInfinite numberK iterationLinear mixture modelMean squared errorSpecific probabilistic modelMixture modelSignal vectorUnstructured noiseLeast squaresPLS algorithmProbabilistic model
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